MMINi-DASS - Large-scale brain circuit construction and simulation for interconnectivity prediction

Abstract

Event Abstract Back to Event MMINi-DASS - Large-scale brain circuit construction and simulation for interconnectivity prediction Venkateswaran Nagarajan1*, Karthik Srinivasan1, Ashutosh Mohan1, Vijay Daniel1, Vijay R1, Harish Chandran1 and Vignesh J1 1 Waran Research Foundation, India The MMINi-DASS framework[1,2] starts off with an arbitrary biological neural network which is statistically correct for a given brain region and a set of desired dynamics in the form of fMRI BOLD images. The constructed network is then simulated using computationally efficient and realistic models on a custom-made and event-driven simulator. Some models have been designed exclusively for the project while others are optimized implementations of existing models. The dynamics obtained from the simulation are then analyzed during run-time and compared with the desired dynamics by a Simulated Annealing Optimization engine. This then makes changes to the structure of the constructed network and invokes the simulation again, thus starting another loop. Evidently, the most interesting aspect of the MMINi-DASS project is the possibility of gaining structural, biophysical, biochemical and electrical knowledge from merely knowledge of the dynamics of a region initially. Since the simulation is large-scale while also being detailed, the simulator is optimized for handling the large number of generated events efficiently. A Linux-based MPI (Message Passing Interface) cluster is setup. To handle the large amount of data generated, dedicated database architecture is used. The whole system including the API (Application Programming Interface), simulator and analyzer are generic and are based on XML specifications. This eliminates the need to rebuild or reconfigure the system each time the logic for circuit building, models or analysis is modified. A change in the XML specifications automatically ripples throughout the system. Among the most important problems for neuronal network simulations on a cluster are scalability, performance evaluation and improvement. The MMINi-DASS project uses a custom-made Performance Evaluator. This, while measuring the performance and resource utilization during run-time, also provides pointers to how performance scaling can be made linear with respect to problem size and architectural scaling within the current context and limitations. In the context of neuronal simulations, performance evaluation packages like HPL don’t provide accurate estimates. Thus, efforts are underway to use the BENSIM benchmarking package developed in-house for a realistic estimate of efficiency of both the implementation and the architecture. tn_Figure 1 Figure 1